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Record W2121524796 · doi:10.1109/naps.2008.5307327

Optimal wind installation considering capacity and energy credits

2008· article· en· W2121524796 on OpenAlex
Rajesh Karki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerRenewable energyElectric power systemMonte Carlo methodReliability engineeringComputer scienceGridIntermittent energy sourceEnvironmental economicsAutomotive engineeringDistributed generationEngineeringPower (physics)Electrical engineeringEconomics

Abstract

fetched live from OpenAlex

Renewable energy applications are rapidly increasing in electric power generating systems. The key factors influencing their rapid growth are public support due to environmental concerns, leading to governmental policies implemented to promote renewable energy applications. Recent technological developments in wind turbines have resulted in large scale applications including supplying power to the grid system. Various planning and operating problems will arise as wind power penetration continues to increase. It is very important to determine the optimal level of wind penetration in a power system. This paper presents a Monte Carlo simulation method that considers both the economics and reliability aspects in system generation planning in order to determine the optimal wind penetration in a given power system. The technique is applied to test systems in order to illustrate the results. The paper provides useful information to system planners in deciding appropriate wind power penetration for a given system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.176
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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